# Iteration vs Recursion Comparison

At the high level, two approaches are important for solving data structures and algorithms problems: The iterative approach and the Recursive approach.

Iterative problem-solving approaches: Building partial solution using a single loop and nested loops, Two pointers approach, Sliding window approach, Problem-solving using data structures (Hash Table, Stack, Queue, Priority Queue, BST, etc.), BFS traversal, Dynamic programming bottom-up approach, Backtracking using stack, Greedy approach, etc.

Recursive problem-solving approaches: Decrease and conquer, divide and conquer, DFS traversal, Recursive backtracking, Dynamic programming top-down memoization, etc.

One thing is common to both approaches: Repeated execution of a set of instructions until our task is done. But there are a lot of differences between them in terms of thought process, implementation approach, analysis techniques, boundary conditions, code complexity, code performance, etc.

### Differences in terms of thought process

Sometimes, we can solve a problem using both approaches, but sometimes only one approach works better. Why do such things happen? The idea would be simple: sometimes iteration is easy to think, sometimes recursion is easy to think but sometimes both. It depends on the nature of the problem.

Iteration is more challenging to understand in the case of some algorithms. In other words, an algorithm that can be expressed naturally using recursion may not be as easy to understand if described iteratively.

• For example, algorithms like binary search, merge sort, quick sort, DFS Traveral of graph and tree, etc., are easy to think of using recursion. It will take some time for us to implement using iteration, and the code will be a little complex also.
• Approaches like backtracking and data structure like trees are recursive. So they are easier to understand using recursion.

### Differences in terms of implementation

• Iteration is implemented using loops and defined by initialization and increment of loop variables, loop body, and termination condition. Recursion is implemented using function calls and determined by the number of sub-problems, function parameters, base case, and additional operations at each recursion step. Here function calls must be stored in a call stack to allow the return back to the caller functions.
• Every recursion can be modeled iteratively using a stack (that’s what the compiler will ultimately do in the background). Most of the time, iterative implementation using a stack becomes complex due to many input variables, additional operations, and the complex nature of recursive calls. So changing a recursive algorithm to an iterative algorithm might need a lot of code modification, making our code more complicated or less maintainable.
• Sometimes, a recursive solution can be often implemented iteratively without using a stack. The classic example of this is Fibonacci numbers, finding factorial, binary search, etc.
• In some situations, recursion is simple, and we can easily convert it into an iterative code using a stack. One idea works best: we need to mimic what the compiler does! An excellent example of this is iterative DFS traversal using stack.

### Differences in terms of code execution

• An iterative process is about repeatedly executing some code statements using a loop until our problem gets solved. On the other hand, the recursive process involves solving the problem using the smaller sub-problems until we reach the smallest version of the problem, i.e., the base case.
• Recursion is usually slower due to the extra overhead of function calls and recursion call stack. But iteration does not involve any such overhead.

### Differences in terms of code error

• An infinite loop may occur in iteration due to a mistake in the assignment or increment of loop variable or a wrong terminating condition. This will consume system resources like processor time or memory and stop the program execution.
• In recursion, an infinite recursion may occur due to the absence of a base case or a wrong base case. It will cause a stack overflow scenario, leading to a CPU crash.
• Sometimes we encounter small but critical mistakes in iteration: off by one error! A similar error scenario arises in recursion when we pass the wrong value to the function parameters.

### Differences in terms of code analysis

We analyze iteration using counting loop iteration and multiplying it with the complexity of code executed at each iteration. So most of the time, the analysis of an iterative code is simple. There are some exceptions when counting loop iteration is tricky, or operations performed by the nested loop are lower than expected. Think!

The fundamental method of recursion analysis is the recursion tree method, where we draw the recursion tree and sum the total cost of the recursion at each level. So most of the time, analyzing recursive code is difficult due to the complex recurrence relations.

## Comparing recursion and iteration using code examples

Using various examples of recursive and iterative solutions to the same question, let's understand this difference. It will be an excellent exercise to understand the comparison between the solution thought process, performance, and code simplicity.

### Example 1: Finding the factorial of a number

Both iterative and recursive solutions are easy to think of, and recursive code looks a little more straightforward compared to the iterative code. On another side, the time complexity of both codes is O(n), but recursive implementation will take O(n) extra space for the call stack.

Recursive Pseudocode

``````int fact(int n)
{
if (n == 0)
return 1
return n * fact(n - 1)
}
``````

Iterative Pseudocode

``````int fact(int n)
{
int res = 1
for (int i = 2; i <= n; i = i + 1)
res = res * i
return res
}
``````

### Example 2: Finding nth Fibonacci

Here recursive solution is simple and easy to think of due to the natural definition of the Fibonacci number. But recursive code works in O(2^n) time and O(n) space (call stack), which is highly inefficient due to the repeated sub-problem scenario. But on another side, iterative code is highly efficient and works in O(n) time and O(1) space.

Recursive Pseudocode

``````int fib(int n)
{
if(n <= 1)
return n
return fib(n - 1) + fib(n - 2)
}
``````

Iterative Pseudocode

``````int fib(int n)
{
if (n <= 1 )
return n
a = 0, b = 1
for (int i = 2; i <= n; i = i + 1)
{
c = a + b
a = b
b = c
}
return c
}
``````

### Example 3: Binary Search

Here both solution code looks simple, but the recursive solution looks natural because we can quickly think of solving the larger problem using the solution of a smaller problem of binary search. On the other hand, an iterative solution is a loop-based simulation(without using a stack!) of the recursive solution, which is tricky to think of!

Both codes work in O(logn) time, but the recursive solution will take O(logn) extra space for the call stack. Conversely, iterative code has no overhead of recursive calls and works in O(1) space. So iterative solution would be the best choice in terms of efficiency.

Recursive Pseudocode

``````int binarySearch(int X[], int l, int r, int key)
{
if (l > r)
return -1
else
{
int mid = l + (r - l) / 2
if (X[mid] == key)
return mid
if (X[mid] > key)
return binarySearch(X, l, mid - 1, key)
else
return binarySearch(X, mid + 1, r, key)
}
}
``````

Iterative Pseudocode

``````int binarySearch(int X[], int l, int r, int key)
{
while (l <= r)
{
int mid = l + (r - l) / 2
if (X[mid] == key)
return mid
if (X[mid] < key)
l = mid + 1
else
r = mid - 1
}
return -1
}
``````

### Example 4: Insertion Sort

Both solutions work in O(n^2) time, but the recursive solution will take O(n) extra space for the call stack. Iterative code has no overhead of recursive calls and is easy to think. So iterative solution would be the best choice in terms of efficiency and thinking.

Iterative Pseudocode

``````void insertionSort(int X[], int n)
{
for (int i = 1; i < n; i = i + 1)
{
int currValue = X[i]
int j = i - 1
while (j >= 0 && X[j] > currValue)
{
X[j + 1] = X[j]
j = j - 1
}
X[j + 1] = currValue
}
}
``````

Recursive Pseudocode

``````void insertionSort(int X[], int n)
{
if (n <= 1)
return
insertionSort(X, n - 1)
int temp = X[n - 1]
int i = n - 2
while (i >= 0 && X[i] > temp)
{
X[i + 1] = X[i]
i = i - 1
}
X[i + 1] = temp
}
``````

### Example 5: Pre-order tree traversal

Here recursive code is simple and easy to visualize, i.e., one function parameter and 3–4 lines of code. On another side, recursive preorder traversal is tail-recursive, i.e., there is no extra code operation after the final recursive call. So we can easily implement it iteratively using a stack, which is a little complex in code implementation.

The good thing is that both codes have O(n) time and space complexity. So if we put little effort into understanding iterative code, it can be highly useful in problem-solving.

Recursive Pseudocode

``````void preorder(TreeNode root)
{
if (root == NULL)
return
process(root->data)
preorder(root->left)
preorder(root->right)
}
``````

Iterative Pseudocode

``````void iterativePreorder(TreeNode root)
{
if(root == NULL)
return
Stack<TreeNode> treeStack
TreeNode currNode = root
TreeNode prevNode = NULL
while (treeStack.empty() == false || currNode != NULL)
{
if (currNode != NULL)
{
process(currNode->data)
treeStack.push(currNode)
currNode = currNode->left
}
else
{
prevNode = treeStack.pop()
currNode = prevNode->right
}
}
}
``````

### Critical ideas to think about!

• If recursion is usually slower, why do we sometimes prefer it over iteration?
• Is there any rule of thumb to convert a recursive code into an iterative code?
• Is it correct to say that a loop could be used everywhere recursion is used?

Enjoy learning, Enjoy algorithms!